1. Turning Data Science into CO2 savings (Daniel Rohr)

“Can I drive more efficiently?” was the question to be answered at the beginning of all data science efforts at Tracks. This is a typical real-life question that is difficult to solve with ML methods. The answer to this question has sparked a number of follow up business questions that we are tackling with Tracks’ complex AI system. In this session you will learn how to turn ML into saved CO2.

2. Finding Sensitive Intervention Points by Mapping the Global Fossil Fuel Supply Chain (Jannes Klaas)

Over 84% of the worlds energy comes from fossil fuels. But oil, gas and coal are also a major transport good. By identifying the networked structure of the global fossil fuel supply chain we can identify sensitive intervention points. We present our work in creating an asset level network of the global fossil fuel supply chain. We will highlight some early research avenues enabled by this dataset, including the use of new modelling techniques.

3. Applying Machine Learning to Solve the Ocean-Bound Plastic Crisis (Bosse Rothe)

11 million tonnes of plastic are estimated to end up in our oceans every year. Cleanhub developed a digital solution that can scale plastic collection in high-impact countries. To deliver digital evidence about how much plastic was collected, all collection partners use CLEANHUB’s software to track and trace the entire recovery process. Detecting anomalies is key to mitigate fraudulent behaviours. We will discuss the problem, opportunities and the role of machine learning.